{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0                       1  2        3       4       5      6   7  \\\n",
       "0  2019162542  /front-api/bill/create  8  1057.31   88.75  177.72  132.0  60   \n",
       "1      162644  /front-api/bill/create  5   749.12  103.79  240.38  149.0  60   \n",
       "2      162742  /front-api/bill/create  5   845.84  136.31  225.73  169.0  60   \n",
       "3      162808  /front-api/bill/create  9  1305.52   90.12  196.61  145.0  60   \n",
       "4      162943  /front-api/bill/create  3   568.89  138.45  232.02  189.0  60   \n",
       "\n",
       "                     8  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./log.txt', header = None, sep = '\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','created_at']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1      162644  /front-api/bill/create      5        749.12        103.79   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2018-11-01 00:01:07  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
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       "      <th>created_at</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41390</th>\n",
       "      <td>3596366</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>150.58</td>\n",
       "      <td>150.58</td>\n",
       "      <td>150.58</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-12-19 12:27:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125336</th>\n",
       "      <td>9281862</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>2572.24</td>\n",
       "      <td>100.22</td>\n",
       "      <td>763.62</td>\n",
       "      <td>321.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-30 13:17:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77059</th>\n",
       "      <td>5968091</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>14</td>\n",
       "      <td>1999.19</td>\n",
       "      <td>93.70</td>\n",
       "      <td>295.21</td>\n",
       "      <td>142.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-29 21:22:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89930</th>\n",
       "      <td>6827077</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>125.87</td>\n",
       "      <td>125.87</td>\n",
       "      <td>125.87</td>\n",
       "      <td>125.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-02-14 12:14:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166809</th>\n",
       "      <td>12457618</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>7</td>\n",
       "      <td>1321.65</td>\n",
       "      <td>88.25</td>\n",
       "      <td>342.18</td>\n",
       "      <td>188.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-16 18:10:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                     api  count  res_time_sum  res_time_min  \\\n",
       "41390    3596366  /front-api/bill/create      1        150.58        150.58   \n",
       "125336   9281862  /front-api/bill/create      8       2572.24        100.22   \n",
       "77059    5968091  /front-api/bill/create     14       1999.19         93.70   \n",
       "89930    6827077  /front-api/bill/create      1        125.87        125.87   \n",
       "166809  12457618  /front-api/bill/create      7       1321.65         88.25   \n",
       "\n",
       "        res_time_max  res_time_avg  interval           created_at  \n",
       "41390         150.58         150.0        60  2018-12-19 12:27:37  \n",
       "125336        763.62         321.0        60  2019-03-30 13:17:14  \n",
       "77059         295.21         142.0        60  2019-01-29 21:22:47  \n",
       "89930         125.87         125.0        60  2019-02-14 12:14:11  \n",
       "166809        342.18         188.0        60  2019-05-16 18:10:07  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179496, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                int64\n",
       "api              object\n",
       "count             int64\n",
       "res_time_sum    float64\n",
       "res_time_min    float64\n",
       "res_time_max    float64\n",
       "res_time_avg    float64\n",
       "interval          int64\n",
       "created_at       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      "id              179496 non-null int64\n",
      "api             179496 non-null object\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() #查看内存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     179496\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                      179496\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['api'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
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       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  2019162542      8       1057.31         88.75        177.72         132.0   \n",
       "1      162644      5        749.12        103.79        240.38         149.0   \n",
       "\n",
       "   interval           created_at  \n",
       "0        60  2018-11-01 00:00:07  \n",
       "1        60  2018-11-01 00:01:07  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop('api',axis = 1)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 8 columns):\n",
      "id              179496 non-null int64\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 11.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  179496\n",
       "unique                 179496\n",
       "top       2019-05-25 18:23:16\n",
       "freq                        1\n",
       "Name: created_at, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['created_at'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
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       "      <th>153089</th>\n",
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       "      <td>6</td>\n",
       "      <td>2105.08</td>\n",
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       "      <td>60</td>\n",
       "      <td>2019-05-01 00:00:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153090</th>\n",
       "      <td>11406236</td>\n",
       "      <td>7</td>\n",
       "      <td>2579.11</td>\n",
       "      <td>76.55</td>\n",
       "      <td>987.47</td>\n",
       "      <td>368.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:01:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153091</th>\n",
       "      <td>11406347</td>\n",
       "      <td>7</td>\n",
       "      <td>1277.79</td>\n",
       "      <td>109.65</td>\n",
       "      <td>236.73</td>\n",
       "      <td>182.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:02:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153092</th>\n",
       "      <td>11406446</td>\n",
       "      <td>7</td>\n",
       "      <td>2137.20</td>\n",
       "      <td>131.55</td>\n",
       "      <td>920.52</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:03:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153093</th>\n",
       "      <td>11406488</td>\n",
       "      <td>13</td>\n",
       "      <td>2948.70</td>\n",
       "      <td>86.42</td>\n",
       "      <td>491.31</td>\n",
       "      <td>226.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:04:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153094</th>\n",
       "      <td>11406599</td>\n",
       "      <td>6</td>\n",
       "      <td>2463.78</td>\n",
       "      <td>137.75</td>\n",
       "      <td>1445.82</td>\n",
       "      <td>410.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:05:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153095</th>\n",
       "      <td>11406661</td>\n",
       "      <td>6</td>\n",
       "      <td>2875.67</td>\n",
       "      <td>166.32</td>\n",
       "      <td>1304.41</td>\n",
       "      <td>479.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:06:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153096</th>\n",
       "      <td>11406751</td>\n",
       "      <td>8</td>\n",
       "      <td>1764.17</td>\n",
       "      <td>93.63</td>\n",
       "      <td>425.96</td>\n",
       "      <td>220.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:07:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153097</th>\n",
       "      <td>11406812</td>\n",
       "      <td>8</td>\n",
       "      <td>2577.12</td>\n",
       "      <td>148.68</td>\n",
       "      <td>864.03</td>\n",
       "      <td>322.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:08:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153098</th>\n",
       "      <td>11406929</td>\n",
       "      <td>5</td>\n",
       "      <td>929.82</td>\n",
       "      <td>67.42</td>\n",
       "      <td>413.51</td>\n",
       "      <td>185.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:09:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153099</th>\n",
       "      <td>11407005</td>\n",
       "      <td>4</td>\n",
       "      <td>912.60</td>\n",
       "      <td>171.17</td>\n",
       "      <td>297.85</td>\n",
       "      <td>228.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:10:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153100</th>\n",
       "      <td>11407047</td>\n",
       "      <td>2</td>\n",
       "      <td>279.56</td>\n",
       "      <td>123.47</td>\n",
       "      <td>156.09</td>\n",
       "      <td>139.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:11:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153101</th>\n",
       "      <td>11407133</td>\n",
       "      <td>4</td>\n",
       "      <td>714.73</td>\n",
       "      <td>125.50</td>\n",
       "      <td>226.84</td>\n",
       "      <td>178.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:12:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153102</th>\n",
       "      <td>11407234</td>\n",
       "      <td>5</td>\n",
       "      <td>1285.32</td>\n",
       "      <td>81.12</td>\n",
       "      <td>436.79</td>\n",
       "      <td>257.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:13:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153103</th>\n",
       "      <td>11407282</td>\n",
       "      <td>6</td>\n",
       "      <td>1425.18</td>\n",
       "      <td>99.28</td>\n",
       "      <td>571.42</td>\n",
       "      <td>237.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:14:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153104</th>\n",
       "      <td>11407386</td>\n",
       "      <td>5</td>\n",
       "      <td>947.69</td>\n",
       "      <td>97.91</td>\n",
       "      <td>313.41</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:15:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153105</th>\n",
       "      <td>11407436</td>\n",
       "      <td>4</td>\n",
       "      <td>1000.06</td>\n",
       "      <td>157.33</td>\n",
       "      <td>335.86</td>\n",
       "      <td>250.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:16:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153106</th>\n",
       "      <td>11407531</td>\n",
       "      <td>2</td>\n",
       "      <td>279.14</td>\n",
       "      <td>117.30</td>\n",
       "      <td>161.84</td>\n",
       "      <td>139.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:17:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153107</th>\n",
       "      <td>11407611</td>\n",
       "      <td>7</td>\n",
       "      <td>994.75</td>\n",
       "      <td>73.33</td>\n",
       "      <td>229.60</td>\n",
       "      <td>142.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:18:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153108</th>\n",
       "      <td>11407632</td>\n",
       "      <td>8</td>\n",
       "      <td>2207.46</td>\n",
       "      <td>76.31</td>\n",
       "      <td>1114.91</td>\n",
       "      <td>275.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:19:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153109</th>\n",
       "      <td>11407730</td>\n",
       "      <td>6</td>\n",
       "      <td>1244.12</td>\n",
       "      <td>119.18</td>\n",
       "      <td>400.02</td>\n",
       "      <td>207.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:20:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153110</th>\n",
       "      <td>11407845</td>\n",
       "      <td>4</td>\n",
       "      <td>892.43</td>\n",
       "      <td>103.66</td>\n",
       "      <td>374.82</td>\n",
       "      <td>223.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:21:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153111</th>\n",
       "      <td>11407897</td>\n",
       "      <td>4</td>\n",
       "      <td>1093.26</td>\n",
       "      <td>66.57</td>\n",
       "      <td>434.01</td>\n",
       "      <td>273.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:22:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153112</th>\n",
       "      <td>11407980</td>\n",
       "      <td>6</td>\n",
       "      <td>1116.52</td>\n",
       "      <td>89.45</td>\n",
       "      <td>485.38</td>\n",
       "      <td>186.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:23:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153113</th>\n",
       "      <td>11408036</td>\n",
       "      <td>6</td>\n",
       "      <td>770.21</td>\n",
       "      <td>77.44</td>\n",
       "      <td>217.87</td>\n",
       "      <td>128.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:24:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153114</th>\n",
       "      <td>11408107</td>\n",
       "      <td>6</td>\n",
       "      <td>1308.97</td>\n",
       "      <td>89.86</td>\n",
       "      <td>399.41</td>\n",
       "      <td>218.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:25:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153115</th>\n",
       "      <td>11408194</td>\n",
       "      <td>5</td>\n",
       "      <td>848.25</td>\n",
       "      <td>108.51</td>\n",
       "      <td>260.88</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:26:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153116</th>\n",
       "      <td>11408253</td>\n",
       "      <td>5</td>\n",
       "      <td>2407.06</td>\n",
       "      <td>90.05</td>\n",
       "      <td>1186.62</td>\n",
       "      <td>481.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:27:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153117</th>\n",
       "      <td>11408357</td>\n",
       "      <td>4</td>\n",
       "      <td>710.47</td>\n",
       "      <td>163.89</td>\n",
       "      <td>191.80</td>\n",
       "      <td>177.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:28:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153118</th>\n",
       "      <td>11408389</td>\n",
       "      <td>7</td>\n",
       "      <td>1675.60</td>\n",
       "      <td>110.26</td>\n",
       "      <td>619.54</td>\n",
       "      <td>239.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:29:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153943</th>\n",
       "      <td>11473695</td>\n",
       "      <td>3</td>\n",
       "      <td>471.28</td>\n",
       "      <td>86.32</td>\n",
       "      <td>194.36</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:30:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153944</th>\n",
       "      <td>11473734</td>\n",
       "      <td>9</td>\n",
       "      <td>1753.33</td>\n",
       "      <td>81.64</td>\n",
       "      <td>545.84</td>\n",
       "      <td>194.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:31:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153945</th>\n",
       "      <td>11473812</td>\n",
       "      <td>3</td>\n",
       "      <td>566.92</td>\n",
       "      <td>166.21</td>\n",
       "      <td>213.47</td>\n",
       "      <td>188.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:32:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153946</th>\n",
       "      <td>11473844</td>\n",
       "      <td>2</td>\n",
       "      <td>258.84</td>\n",
       "      <td>65.36</td>\n",
       "      <td>193.48</td>\n",
       "      <td>129.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:33:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153947</th>\n",
       "      <td>11473942</td>\n",
       "      <td>2</td>\n",
       "      <td>300.97</td>\n",
       "      <td>138.49</td>\n",
       "      <td>162.48</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:34:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153948</th>\n",
       "      <td>11474015</td>\n",
       "      <td>6</td>\n",
       "      <td>792.55</td>\n",
       "      <td>69.46</td>\n",
       "      <td>239.17</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:35:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153949</th>\n",
       "      <td>11474088</td>\n",
       "      <td>6</td>\n",
       "      <td>1157.81</td>\n",
       "      <td>124.12</td>\n",
       "      <td>423.91</td>\n",
       "      <td>192.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:36:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153950</th>\n",
       "      <td>11474163</td>\n",
       "      <td>2</td>\n",
       "      <td>433.06</td>\n",
       "      <td>98.41</td>\n",
       "      <td>334.65</td>\n",
       "      <td>216.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:37:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153951</th>\n",
       "      <td>11474223</td>\n",
       "      <td>4</td>\n",
       "      <td>425.51</td>\n",
       "      <td>75.69</td>\n",
       "      <td>144.11</td>\n",
       "      <td>106.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:38:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153952</th>\n",
       "      <td>11474299</td>\n",
       "      <td>4</td>\n",
       "      <td>604.55</td>\n",
       "      <td>103.00</td>\n",
       "      <td>191.69</td>\n",
       "      <td>151.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:39:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153953</th>\n",
       "      <td>11474340</td>\n",
       "      <td>4</td>\n",
       "      <td>599.14</td>\n",
       "      <td>141.13</td>\n",
       "      <td>162.50</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:40:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153954</th>\n",
       "      <td>11474412</td>\n",
       "      <td>3</td>\n",
       "      <td>519.14</td>\n",
       "      <td>130.28</td>\n",
       "      <td>219.06</td>\n",
       "      <td>173.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:41:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153955</th>\n",
       "      <td>11474510</td>\n",
       "      <td>1</td>\n",
       "      <td>336.79</td>\n",
       "      <td>336.79</td>\n",
       "      <td>336.79</td>\n",
       "      <td>336.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:42:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153956</th>\n",
       "      <td>11474559</td>\n",
       "      <td>8</td>\n",
       "      <td>1741.96</td>\n",
       "      <td>83.68</td>\n",
       "      <td>592.15</td>\n",
       "      <td>217.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:43:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153957</th>\n",
       "      <td>11474630</td>\n",
       "      <td>5</td>\n",
       "      <td>573.94</td>\n",
       "      <td>75.98</td>\n",
       "      <td>160.20</td>\n",
       "      <td>114.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:44:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153958</th>\n",
       "      <td>11474719</td>\n",
       "      <td>5</td>\n",
       "      <td>1221.15</td>\n",
       "      <td>74.16</td>\n",
       "      <td>726.07</td>\n",
       "      <td>244.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:45:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153959</th>\n",
       "      <td>11474783</td>\n",
       "      <td>7</td>\n",
       "      <td>775.40</td>\n",
       "      <td>69.56</td>\n",
       "      <td>165.25</td>\n",
       "      <td>110.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:46:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153960</th>\n",
       "      <td>11474860</td>\n",
       "      <td>5</td>\n",
       "      <td>1109.98</td>\n",
       "      <td>114.90</td>\n",
       "      <td>406.98</td>\n",
       "      <td>221.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:47:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153961</th>\n",
       "      <td>11474885</td>\n",
       "      <td>5</td>\n",
       "      <td>563.23</td>\n",
       "      <td>83.24</td>\n",
       "      <td>171.42</td>\n",
       "      <td>112.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:48:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153962</th>\n",
       "      <td>11474974</td>\n",
       "      <td>3</td>\n",
       "      <td>351.08</td>\n",
       "      <td>69.84</td>\n",
       "      <td>148.27</td>\n",
       "      <td>117.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:49:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153963</th>\n",
       "      <td>11475041</td>\n",
       "      <td>4</td>\n",
       "      <td>609.49</td>\n",
       "      <td>89.03</td>\n",
       "      <td>235.60</td>\n",
       "      <td>152.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:50:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153964</th>\n",
       "      <td>11475066</td>\n",
       "      <td>4</td>\n",
       "      <td>1285.34</td>\n",
       "      <td>154.31</td>\n",
       "      <td>538.34</td>\n",
       "      <td>321.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:51:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153965</th>\n",
       "      <td>11475136</td>\n",
       "      <td>4</td>\n",
       "      <td>884.68</td>\n",
       "      <td>111.59</td>\n",
       "      <td>468.82</td>\n",
       "      <td>221.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:52:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153966</th>\n",
       "      <td>11475226</td>\n",
       "      <td>7</td>\n",
       "      <td>1377.46</td>\n",
       "      <td>133.20</td>\n",
       "      <td>248.60</td>\n",
       "      <td>196.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:53:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153967</th>\n",
       "      <td>11475311</td>\n",
       "      <td>4</td>\n",
       "      <td>656.67</td>\n",
       "      <td>126.56</td>\n",
       "      <td>243.48</td>\n",
       "      <td>164.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:54:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153968</th>\n",
       "      <td>11475363</td>\n",
       "      <td>6</td>\n",
       "      <td>1083.97</td>\n",
       "      <td>70.85</td>\n",
       "      <td>262.22</td>\n",
       "      <td>180.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:55:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153969</th>\n",
       "      <td>11475483</td>\n",
       "      <td>4</td>\n",
       "      <td>840.00</td>\n",
       "      <td>117.31</td>\n",
       "      <td>382.63</td>\n",
       "      <td>210.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:56:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153970</th>\n",
       "      <td>11475550</td>\n",
       "      <td>2</td>\n",
       "      <td>295.51</td>\n",
       "      <td>101.71</td>\n",
       "      <td>193.80</td>\n",
       "      <td>147.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:57:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153971</th>\n",
       "      <td>11475597</td>\n",
       "      <td>2</td>\n",
       "      <td>431.99</td>\n",
       "      <td>84.43</td>\n",
       "      <td>347.56</td>\n",
       "      <td>215.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:58:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153972</th>\n",
       "      <td>11475664</td>\n",
       "      <td>3</td>\n",
       "      <td>428.84</td>\n",
       "      <td>103.58</td>\n",
       "      <td>206.57</td>\n",
       "      <td>142.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:59:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>884 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "153089  11406128      6       2105.08        125.74        992.46   \n",
       "153090  11406236      7       2579.11         76.55        987.47   \n",
       "153091  11406347      7       1277.79        109.65        236.73   \n",
       "153092  11406446      7       2137.20        131.55        920.52   \n",
       "153093  11406488     13       2948.70         86.42        491.31   \n",
       "153094  11406599      6       2463.78        137.75       1445.82   \n",
       "153095  11406661      6       2875.67        166.32       1304.41   \n",
       "153096  11406751      8       1764.17         93.63        425.96   \n",
       "153097  11406812      8       2577.12        148.68        864.03   \n",
       "153098  11406929      5        929.82         67.42        413.51   \n",
       "153099  11407005      4        912.60        171.17        297.85   \n",
       "153100  11407047      2        279.56        123.47        156.09   \n",
       "153101  11407133      4        714.73        125.50        226.84   \n",
       "153102  11407234      5       1285.32         81.12        436.79   \n",
       "153103  11407282      6       1425.18         99.28        571.42   \n",
       "153104  11407386      5        947.69         97.91        313.41   \n",
       "153105  11407436      4       1000.06        157.33        335.86   \n",
       "153106  11407531      2        279.14        117.30        161.84   \n",
       "153107  11407611      7        994.75         73.33        229.60   \n",
       "153108  11407632      8       2207.46         76.31       1114.91   \n",
       "153109  11407730      6       1244.12        119.18        400.02   \n",
       "153110  11407845      4        892.43        103.66        374.82   \n",
       "153111  11407897      4       1093.26         66.57        434.01   \n",
       "153112  11407980      6       1116.52         89.45        485.38   \n",
       "153113  11408036      6        770.21         77.44        217.87   \n",
       "153114  11408107      6       1308.97         89.86        399.41   \n",
       "153115  11408194      5        848.25        108.51        260.88   \n",
       "153116  11408253      5       2407.06         90.05       1186.62   \n",
       "153117  11408357      4        710.47        163.89        191.80   \n",
       "153118  11408389      7       1675.60        110.26        619.54   \n",
       "...          ...    ...           ...           ...           ...   \n",
       "153943  11473695      3        471.28         86.32        194.36   \n",
       "153944  11473734      9       1753.33         81.64        545.84   \n",
       "153945  11473812      3        566.92        166.21        213.47   \n",
       "153946  11473844      2        258.84         65.36        193.48   \n",
       "153947  11473942      2        300.97        138.49        162.48   \n",
       "153948  11474015      6        792.55         69.46        239.17   \n",
       "153949  11474088      6       1157.81        124.12        423.91   \n",
       "153950  11474163      2        433.06         98.41        334.65   \n",
       "153951  11474223      4        425.51         75.69        144.11   \n",
       "153952  11474299      4        604.55        103.00        191.69   \n",
       "153953  11474340      4        599.14        141.13        162.50   \n",
       "153954  11474412      3        519.14        130.28        219.06   \n",
       "153955  11474510      1        336.79        336.79        336.79   \n",
       "153956  11474559      8       1741.96         83.68        592.15   \n",
       "153957  11474630      5        573.94         75.98        160.20   \n",
       "153958  11474719      5       1221.15         74.16        726.07   \n",
       "153959  11474783      7        775.40         69.56        165.25   \n",
       "153960  11474860      5       1109.98        114.90        406.98   \n",
       "153961  11474885      5        563.23         83.24        171.42   \n",
       "153962  11474974      3        351.08         69.84        148.27   \n",
       "153963  11475041      4        609.49         89.03        235.60   \n",
       "153964  11475066      4       1285.34        154.31        538.34   \n",
       "153965  11475136      4        884.68        111.59        468.82   \n",
       "153966  11475226      7       1377.46        133.20        248.60   \n",
       "153967  11475311      4        656.67        126.56        243.48   \n",
       "153968  11475363      6       1083.97         70.85        262.22   \n",
       "153969  11475483      4        840.00        117.31        382.63   \n",
       "153970  11475550      2        295.51        101.71        193.80   \n",
       "153971  11475597      2        431.99         84.43        347.56   \n",
       "153972  11475664      3        428.84        103.58        206.57   \n",
       "\n",
       "        res_time_avg  interval           created_at  \n",
       "153089         350.0        60  2019-05-01 00:00:48  \n",
       "153090         368.0        60  2019-05-01 00:01:48  \n",
       "153091         182.0        60  2019-05-01 00:02:48  \n",
       "153092         305.0        60  2019-05-01 00:03:48  \n",
       "153093         226.0        60  2019-05-01 00:04:48  \n",
       "153094         410.0        60  2019-05-01 00:05:48  \n",
       "153095         479.0        60  2019-05-01 00:06:48  \n",
       "153096         220.0        60  2019-05-01 00:07:48  \n",
       "153097         322.0        60  2019-05-01 00:08:48  \n",
       "153098         185.0        60  2019-05-01 00:09:48  \n",
       "153099         228.0        60  2019-05-01 00:10:48  \n",
       "153100         139.0        60  2019-05-01 00:11:48  \n",
       "153101         178.0        60  2019-05-01 00:12:48  \n",
       "153102         257.0        60  2019-05-01 00:13:48  \n",
       "153103         237.0        60  2019-05-01 00:14:48  \n",
       "153104         189.0        60  2019-05-01 00:15:48  \n",
       "153105         250.0        60  2019-05-01 00:16:48  \n",
       "153106         139.0        60  2019-05-01 00:17:48  \n",
       "153107         142.0        60  2019-05-01 00:18:48  \n",
       "153108         275.0        60  2019-05-01 00:19:48  \n",
       "153109         207.0        60  2019-05-01 00:20:48  \n",
       "153110         223.0        60  2019-05-01 00:21:48  \n",
       "153111         273.0        60  2019-05-01 00:22:48  \n",
       "153112         186.0        60  2019-05-01 00:23:48  \n",
       "153113         128.0        60  2019-05-01 00:24:48  \n",
       "153114         218.0        60  2019-05-01 00:25:48  \n",
       "153115         169.0        60  2019-05-01 00:26:48  \n",
       "153116         481.0        60  2019-05-01 00:27:48  \n",
       "153117         177.0        60  2019-05-01 00:28:48  \n",
       "153118         239.0        60  2019-05-01 00:29:48  \n",
       "...              ...       ...                  ...  \n",
       "153943         157.0        60  2019-05-01 23:30:49  \n",
       "153944         194.0        60  2019-05-01 23:31:49  \n",
       "153945         188.0        60  2019-05-01 23:32:49  \n",
       "153946         129.0        60  2019-05-01 23:33:49  \n",
       "153947         150.0        60  2019-05-01 23:34:49  \n",
       "153948         132.0        60  2019-05-01 23:35:49  \n",
       "153949         192.0        60  2019-05-01 23:36:49  \n",
       "153950         216.0        60  2019-05-01 23:37:49  \n",
       "153951         106.0        60  2019-05-01 23:38:49  \n",
       "153952         151.0        60  2019-05-01 23:39:49  \n",
       "153953         149.0        60  2019-05-01 23:40:49  \n",
       "153954         173.0        60  2019-05-01 23:41:49  \n",
       "153955         336.0        60  2019-05-01 23:42:49  \n",
       "153956         217.0        60  2019-05-01 23:43:49  \n",
       "153957         114.0        60  2019-05-01 23:44:49  \n",
       "153958         244.0        60  2019-05-01 23:45:49  \n",
       "153959         110.0        60  2019-05-01 23:46:49  \n",
       "153960         221.0        60  2019-05-01 23:47:49  \n",
       "153961         112.0        60  2019-05-01 23:48:49  \n",
       "153962         117.0        60  2019-05-01 23:49:49  \n",
       "153963         152.0        60  2019-05-01 23:50:49  \n",
       "153964         321.0        60  2019-05-01 23:51:49  \n",
       "153965         221.0        60  2019-05-01 23:52:49  \n",
       "153966         196.0        60  2019-05-01 23:53:49  \n",
       "153967         164.0        60  2019-05-01 23:54:49  \n",
       "153968         180.0        60  2019-05-01 23:55:49  \n",
       "153969         210.0        60  2019-05-01 23:56:49  \n",
       "153970         147.0        60  2019-05-01 23:57:49  \n",
       "153971         215.0        60  2019-05-01 23:58:49  \n",
       "153972         142.0        60  2019-05-01 23:59:49  \n",
       "\n",
       "[884 rows x 8 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.created_at >= '2019-05-01') & (df.created_at < '2019-05-02')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=179496, step=1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = df.created_at"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 179496 entries, 2018-11-01 00:00:07 to 2019-05-30 23:10:21\n",
      "Data columns (total 8 columns):\n",
      "id              179496 non-null int64\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2018-11-01 00:00:07', '2018-11-01 00:01:07', '2018-11-01 00:02:07',\n",
       "       '2018-11-01 00:03:07', '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "       '2018-11-01 00:06:07', '2018-11-01 00:07:07', '2018-11-01 00:08:07',\n",
       "       '2018-11-01 00:09:07',\n",
       "       ...\n",
       "       '2019-05-30 23:01:21', '2019-05-30 23:02:21', '2019-05-30 23:03:21',\n",
       "       '2019-05-30 23:04:21', '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "       '2019-05-30 23:07:21', '2019-05-30 23:08:21', '2019-05-30 23:09:21',\n",
       "       '2019-05-30 23:10:21'],\n",
       "      dtype='object', name='created_at', length=179496)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = pd.to_datetime(df.created_at)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-01 00:00:07', '2018-11-01 00:01:07',\n",
       "               '2018-11-01 00:02:07', '2018-11-01 00:03:07',\n",
       "               '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "               '2018-11-01 00:06:07', '2018-11-01 00:07:07',\n",
       "               '2018-11-01 00:08:07', '2018-11-01 00:09:07',\n",
       "               ...\n",
       "               '2019-05-30 23:01:21', '2019-05-30 23:02:21',\n",
       "               '2019-05-30 23:03:21', '2019-05-30 23:04:21',\n",
       "               '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "               '2019-05-30 23:07:21', '2019-05-30 23:08:21',\n",
       "               '2019-05-30 23:09:21', '2019-05-30 23:10:21'],\n",
       "              dtype='datetime64[ns]', name='created_at', length=179496, freq=None)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interval.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([60], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interval.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
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       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['id','interval'],axis = 1) #因为interval是个常量，无重复值，可以删掉。已经使用了时间序列的index那么id就可以删除掉了\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 179496 entries, 2018-11-01 00:00:07 to 2019-05-30 23:10:21\n",
      "Data columns (total 6 columns):\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 9.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177832</td>\n",
       "      <td>108.419626</td>\n",
       "      <td>359.880374</td>\n",
       "      <td>187.812208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.486073</td>\n",
       "      <td>79.640693</td>\n",
       "      <td>638.919827</td>\n",
       "      <td>224.464813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count   res_time_sum   res_time_min   res_time_max  \\\n",
       "count  179496.000000  179496.000000  179496.000000  179496.000000   \n",
       "mean        7.175909    1393.177832     108.419626     359.880374   \n",
       "std         4.325160    1499.486073      79.640693     638.919827   \n",
       "min         1.000000      36.550000       3.210000      36.550000   \n",
       "25%         4.000000     607.707500      83.410000     198.280000   \n",
       "50%         7.000000    1154.905000      97.120000     256.090000   \n",
       "75%        10.000000    1834.117500     116.990000     374.410000   \n",
       "max        31.000000  142650.550000   18896.640000  142468.270000   \n",
       "\n",
       "        res_time_avg  \n",
       "count  179496.000000  \n",
       "mean      187.812208  \n",
       "std       224.464813  \n",
       "min        36.000000  \n",
       "25%       144.000000  \n",
       "50%       167.000000  \n",
       "75%       202.000000  \n",
       "max     71325.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist(bins = 30) #初步分析count，直方图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1']['count'].plot() #切除一天的数据，绘制api的调用在一天的分布情况\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df['2019-5-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:00:00</th>\n",
       "      <td>4.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 01:00:00</th>\n",
       "      <td>2.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 02:00:00</th>\n",
       "      <td>1.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 05:00:00</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 07:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 08:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 09:00:00</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 10:00:00</th>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 11:00:00</th>\n",
       "      <td>1.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 12:00:00</th>\n",
       "      <td>3.298246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 13:00:00</th>\n",
       "      <td>6.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:00</th>\n",
       "      <td>10.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 15:00:00</th>\n",
       "      <td>12.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 16:00:00</th>\n",
       "      <td>9.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 17:00:00</th>\n",
       "      <td>7.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:00:00</th>\n",
       "      <td>6.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:00:00</th>\n",
       "      <td>9.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:00:00</th>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 21:00:00</th>\n",
       "      <td>10.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 22:00:00</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:00:00</th>\n",
       "      <td>5.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count\n",
       "created_at                    \n",
       "2019-05-01 00:00:00   4.428571\n",
       "2019-05-01 01:00:00   2.272727\n",
       "2019-05-01 02:00:00   1.833333\n",
       "2019-05-01 03:00:00        NaN\n",
       "2019-05-01 04:00:00        NaN\n",
       "2019-05-01 05:00:00   2.000000\n",
       "2019-05-01 06:00:00        NaN\n",
       "2019-05-01 07:00:00        NaN\n",
       "2019-05-01 08:00:00        NaN\n",
       "2019-05-01 09:00:00   1.000000\n",
       "2019-05-01 10:00:00   1.400000\n",
       "2019-05-01 11:00:00   1.604651\n",
       "2019-05-01 12:00:00   3.298246\n",
       "2019-05-01 13:00:00   6.866667\n",
       "2019-05-01 14:00:00  10.483333\n",
       "2019-05-01 15:00:00  12.333333\n",
       "2019-05-01 16:00:00   9.916667\n",
       "2019-05-01 17:00:00   7.666667\n",
       "2019-05-01 18:00:00   6.783333\n",
       "2019-05-01 19:00:00   9.850000\n",
       "2019-05-01 20:00:00  11.000000\n",
       "2019-05-01 21:00:00  10.416667\n",
       "2019-05-01 22:00:00   8.000000\n",
       "2019-05-01 23:00:00   5.083333"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2[['count']].resample('1H').mean()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,3))\n",
    "df2['count'].plot(kind = 'bar')\n",
    "plt.xticks(rotation = 60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aeCAi/gx4FfjTxuYHgcci4gec+4fgdAtdzAF/GxEnU0rl7v8FUnu8bFGSMuGUiyRlwkCXpEwY6JKUCQNdkjJhoEtSJgx0ScqEgS5JmTDQJSkT/we27EYkkQxuhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分析有没有一场时段，访问接口过高，可能就是黑客潮水攻击\n",
    "df['2019-5-1'][['count']].boxplot(showmeans = True, meanline = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 20:47:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3117.20</td>\n",
       "      <td>84.90</td>\n",
       "      <td>260.82</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-01 20:47:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:03:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3706.20</td>\n",
       "      <td>78.12</td>\n",
       "      <td>321.47</td>\n",
       "      <td>176.0</td>\n",
       "      <td>2018-11-01 21:03:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:13:09</th>\n",
       "      <td>24</td>\n",
       "      <td>4602.03</td>\n",
       "      <td>76.31</td>\n",
       "      <td>391.12</td>\n",
       "      <td>191.0</td>\n",
       "      <td>2018-11-01 21:13:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02 21:34:11</th>\n",
       "      <td>30</td>\n",
       "      <td>4610.15</td>\n",
       "      <td>72.49</td>\n",
       "      <td>463.41</td>\n",
       "      <td>153.0</td>\n",
       "      <td>2018-11-02 21:34:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 14:20:13</th>\n",
       "      <td>21</td>\n",
       "      <td>3113.93</td>\n",
       "      <td>74.29</td>\n",
       "      <td>266.20</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-03 14:20:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 20:16:13</th>\n",
       "      <td>21</td>\n",
       "      <td>2992.24</td>\n",
       "      <td>86.28</td>\n",
       "      <td>246.71</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2018-11-03 20:16:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 22:01:13</th>\n",
       "      <td>22</td>\n",
       "      <td>3615.11</td>\n",
       "      <td>108.00</td>\n",
       "      <td>231.49</td>\n",
       "      <td>164.0</td>\n",
       "      <td>2018-11-03 22:01:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 22:42:13</th>\n",
       "      <td>28</td>\n",
       "      <td>4332.65</td>\n",
       "      <td>76.26</td>\n",
       "      <td>263.33</td>\n",
       "      <td>154.0</td>\n",
       "      <td>2018-11-03 22:42:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05 15:49:17</th>\n",
       "      <td>24</td>\n",
       "      <td>3723.64</td>\n",
       "      <td>88.97</td>\n",
       "      <td>280.92</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2018-11-05 15:49:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05 19:33:17</th>\n",
       "      <td>21</td>\n",
       "      <td>2831.71</td>\n",
       "      <td>78.66</td>\n",
       "      <td>170.69</td>\n",
       "      <td>134.0</td>\n",
       "      <td>2018-11-05 19:33:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06 20:49:20</th>\n",
       "      <td>21</td>\n",
       "      <td>3414.39</td>\n",
       "      <td>87.02</td>\n",
       "      <td>257.39</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2018-11-06 20:49:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 15:56:23</th>\n",
       "      <td>21</td>\n",
       "      <td>3356.42</td>\n",
       "      <td>85.43</td>\n",
       "      <td>252.38</td>\n",
       "      <td>159.0</td>\n",
       "      <td>2018-11-08 15:56:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 20:50:23</th>\n",
       "      <td>23</td>\n",
       "      <td>3998.72</td>\n",
       "      <td>90.64</td>\n",
       "      <td>398.60</td>\n",
       "      <td>173.0</td>\n",
       "      <td>2018-11-08 20:50:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 20:51:23</th>\n",
       "      <td>21</td>\n",
       "      <td>3736.10</td>\n",
       "      <td>87.71</td>\n",
       "      <td>327.77</td>\n",
       "      <td>177.0</td>\n",
       "      <td>2018-11-08 20:51:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 20:59:23</th>\n",
       "      <td>21</td>\n",
       "      <td>3161.50</td>\n",
       "      <td>89.86</td>\n",
       "      <td>423.33</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2018-11-08 20:59:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-09 20:49:25</th>\n",
       "      <td>21</td>\n",
       "      <td>3962.84</td>\n",
       "      <td>129.44</td>\n",
       "      <td>322.40</td>\n",
       "      <td>188.0</td>\n",
       "      <td>2018-11-09 20:49:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-09 21:41:25</th>\n",
       "      <td>21</td>\n",
       "      <td>3199.91</td>\n",
       "      <td>75.82</td>\n",
       "      <td>276.96</td>\n",
       "      <td>152.0</td>\n",
       "      <td>2018-11-09 21:41:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-09 22:09:25</th>\n",
       "      <td>22</td>\n",
       "      <td>3582.53</td>\n",
       "      <td>108.02</td>\n",
       "      <td>246.32</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2018-11-09 22:09:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 20:07:26</th>\n",
       "      <td>22</td>\n",
       "      <td>3362.64</td>\n",
       "      <td>80.28</td>\n",
       "      <td>225.21</td>\n",
       "      <td>152.0</td>\n",
       "      <td>2018-11-10 20:07:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 21:17:26</th>\n",
       "      <td>21</td>\n",
       "      <td>3407.67</td>\n",
       "      <td>100.55</td>\n",
       "      <td>263.82</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2018-11-10 21:17:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 21:48:26</th>\n",
       "      <td>21</td>\n",
       "      <td>3274.11</td>\n",
       "      <td>84.12</td>\n",
       "      <td>354.66</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2018-11-10 21:48:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 22:03:26</th>\n",
       "      <td>21</td>\n",
       "      <td>3525.31</td>\n",
       "      <td>119.81</td>\n",
       "      <td>283.33</td>\n",
       "      <td>167.0</td>\n",
       "      <td>2018-11-10 22:03:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 17:02:28</th>\n",
       "      <td>21</td>\n",
       "      <td>3123.46</td>\n",
       "      <td>68.51</td>\n",
       "      <td>359.94</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-11 17:02:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 20:45:28</th>\n",
       "      <td>21</td>\n",
       "      <td>3515.21</td>\n",
       "      <td>85.81</td>\n",
       "      <td>297.33</td>\n",
       "      <td>167.0</td>\n",
       "      <td>2018-11-11 20:45:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 20:48:28</th>\n",
       "      <td>21</td>\n",
       "      <td>3006.97</td>\n",
       "      <td>83.48</td>\n",
       "      <td>353.50</td>\n",
       "      <td>143.0</td>\n",
       "      <td>2018-11-11 20:48:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 22:17:28</th>\n",
       "      <td>23</td>\n",
       "      <td>3709.56</td>\n",
       "      <td>92.62</td>\n",
       "      <td>314.90</td>\n",
       "      <td>161.0</td>\n",
       "      <td>2018-11-11 22:17:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-12 16:28:30</th>\n",
       "      <td>22</td>\n",
       "      <td>3328.76</td>\n",
       "      <td>78.25</td>\n",
       "      <td>257.35</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2018-11-12 16:28:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-12 21:01:30</th>\n",
       "      <td>21</td>\n",
       "      <td>3177.52</td>\n",
       "      <td>92.07</td>\n",
       "      <td>226.59</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2018-11-12 21:01:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-12 21:06:30</th>\n",
       "      <td>21</td>\n",
       "      <td>3887.31</td>\n",
       "      <td>100.05</td>\n",
       "      <td>292.41</td>\n",
       "      <td>185.0</td>\n",
       "      <td>2018-11-12 21:06:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-13 15:51:32</th>\n",
       "      <td>23</td>\n",
       "      <td>3505.80</td>\n",
       "      <td>78.76</td>\n",
       "      <td>249.86</td>\n",
       "      <td>152.0</td>\n",
       "      <td>2018-11-13 15:51:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 16:08:18</th>\n",
       "      <td>27</td>\n",
       "      <td>13177.00</td>\n",
       "      <td>80.89</td>\n",
       "      <td>2768.33</td>\n",
       "      <td>488.0</td>\n",
       "      <td>2019-05-27 16:08:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 18:29:18</th>\n",
       "      <td>23</td>\n",
       "      <td>5264.64</td>\n",
       "      <td>90.01</td>\n",
       "      <td>515.05</td>\n",
       "      <td>228.0</td>\n",
       "      <td>2019-05-27 18:29:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 19:28:18</th>\n",
       "      <td>21</td>\n",
       "      <td>4612.10</td>\n",
       "      <td>93.98</td>\n",
       "      <td>372.50</td>\n",
       "      <td>219.0</td>\n",
       "      <td>2019-05-27 19:28:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 19:49:18</th>\n",
       "      <td>28</td>\n",
       "      <td>5647.21</td>\n",
       "      <td>78.28</td>\n",
       "      <td>648.65</td>\n",
       "      <td>201.0</td>\n",
       "      <td>2019-05-27 19:49:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 20:03:18</th>\n",
       "      <td>21</td>\n",
       "      <td>5146.42</td>\n",
       "      <td>97.18</td>\n",
       "      <td>1250.87</td>\n",
       "      <td>245.0</td>\n",
       "      <td>2019-05-27 20:03:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 20:05:18</th>\n",
       "      <td>21</td>\n",
       "      <td>5242.64</td>\n",
       "      <td>113.51</td>\n",
       "      <td>507.65</td>\n",
       "      <td>249.0</td>\n",
       "      <td>2019-05-27 20:05:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 21:13:18</th>\n",
       "      <td>26</td>\n",
       "      <td>4656.33</td>\n",
       "      <td>102.24</td>\n",
       "      <td>300.69</td>\n",
       "      <td>179.0</td>\n",
       "      <td>2019-05-27 21:13:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 21:16:18</th>\n",
       "      <td>24</td>\n",
       "      <td>5160.23</td>\n",
       "      <td>95.19</td>\n",
       "      <td>538.70</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2019-05-27 21:16:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 21:58:18</th>\n",
       "      <td>25</td>\n",
       "      <td>9587.37</td>\n",
       "      <td>97.71</td>\n",
       "      <td>1304.84</td>\n",
       "      <td>383.0</td>\n",
       "      <td>2019-05-27 21:58:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 22:01:18</th>\n",
       "      <td>21</td>\n",
       "      <td>5813.94</td>\n",
       "      <td>118.05</td>\n",
       "      <td>1130.25</td>\n",
       "      <td>276.0</td>\n",
       "      <td>2019-05-27 22:01:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-28 16:19:19</th>\n",
       "      <td>24</td>\n",
       "      <td>5168.07</td>\n",
       "      <td>94.52</td>\n",
       "      <td>869.76</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2019-05-28 16:19:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-28 20:51:19</th>\n",
       "      <td>23</td>\n",
       "      <td>7090.56</td>\n",
       "      <td>89.50</td>\n",
       "      <td>1613.17</td>\n",
       "      <td>308.0</td>\n",
       "      <td>2019-05-28 20:51:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-28 20:52:19</th>\n",
       "      <td>23</td>\n",
       "      <td>5801.02</td>\n",
       "      <td>77.39</td>\n",
       "      <td>802.72</td>\n",
       "      <td>252.0</td>\n",
       "      <td>2019-05-28 20:52:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-28 22:53:19</th>\n",
       "      <td>22</td>\n",
       "      <td>4000.22</td>\n",
       "      <td>83.75</td>\n",
       "      <td>356.17</td>\n",
       "      <td>181.0</td>\n",
       "      <td>2019-05-28 22:53:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 16:02:20</th>\n",
       "      <td>23</td>\n",
       "      <td>10137.39</td>\n",
       "      <td>96.03</td>\n",
       "      <td>1245.05</td>\n",
       "      <td>440.0</td>\n",
       "      <td>2019-05-29 16:02:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 20:31:20</th>\n",
       "      <td>22</td>\n",
       "      <td>8799.29</td>\n",
       "      <td>105.93</td>\n",
       "      <td>2386.80</td>\n",
       "      <td>399.0</td>\n",
       "      <td>2019-05-29 20:31:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 21:12:20</th>\n",
       "      <td>21</td>\n",
       "      <td>4702.18</td>\n",
       "      <td>97.59</td>\n",
       "      <td>699.19</td>\n",
       "      <td>223.0</td>\n",
       "      <td>2019-05-29 21:12:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 21:34:20</th>\n",
       "      <td>24</td>\n",
       "      <td>5368.32</td>\n",
       "      <td>73.77</td>\n",
       "      <td>742.53</td>\n",
       "      <td>223.0</td>\n",
       "      <td>2019-05-29 21:34:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 22:46:20</th>\n",
       "      <td>21</td>\n",
       "      <td>6892.93</td>\n",
       "      <td>137.39</td>\n",
       "      <td>1309.64</td>\n",
       "      <td>328.0</td>\n",
       "      <td>2019-05-29 22:46:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 23:02:20</th>\n",
       "      <td>24</td>\n",
       "      <td>6331.52</td>\n",
       "      <td>103.16</td>\n",
       "      <td>1196.49</td>\n",
       "      <td>263.0</td>\n",
       "      <td>2019-05-29 23:02:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 20:02:21</th>\n",
       "      <td>24</td>\n",
       "      <td>5038.76</td>\n",
       "      <td>95.34</td>\n",
       "      <td>445.75</td>\n",
       "      <td>209.0</td>\n",
       "      <td>2019-05-30 20:02:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 20:16:21</th>\n",
       "      <td>26</td>\n",
       "      <td>6415.77</td>\n",
       "      <td>85.31</td>\n",
       "      <td>860.74</td>\n",
       "      <td>246.0</td>\n",
       "      <td>2019-05-30 20:16:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:17:21</th>\n",
       "      <td>23</td>\n",
       "      <td>4954.28</td>\n",
       "      <td>97.52</td>\n",
       "      <td>427.05</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2019-05-30 21:17:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:24:21</th>\n",
       "      <td>21</td>\n",
       "      <td>3977.18</td>\n",
       "      <td>93.16</td>\n",
       "      <td>383.06</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2019-05-30 21:24:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:28:21</th>\n",
       "      <td>25</td>\n",
       "      <td>8782.18</td>\n",
       "      <td>98.49</td>\n",
       "      <td>2549.79</td>\n",
       "      <td>351.0</td>\n",
       "      <td>2019-05-30 21:28:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:33:21</th>\n",
       "      <td>27</td>\n",
       "      <td>6456.64</td>\n",
       "      <td>99.65</td>\n",
       "      <td>978.91</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2019-05-30 21:33:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:43:21</th>\n",
       "      <td>21</td>\n",
       "      <td>6371.84</td>\n",
       "      <td>65.98</td>\n",
       "      <td>1175.37</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2019-05-30 21:43:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:47:21</th>\n",
       "      <td>21</td>\n",
       "      <td>3992.83</td>\n",
       "      <td>87.83</td>\n",
       "      <td>440.88</td>\n",
       "      <td>190.0</td>\n",
       "      <td>2019-05-30 21:47:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:53:21</th>\n",
       "      <td>24</td>\n",
       "      <td>8467.02</td>\n",
       "      <td>120.22</td>\n",
       "      <td>1511.17</td>\n",
       "      <td>352.0</td>\n",
       "      <td>2019-05-30 21:53:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:17:21</th>\n",
       "      <td>21</td>\n",
       "      <td>4926.35</td>\n",
       "      <td>85.01</td>\n",
       "      <td>826.90</td>\n",
       "      <td>234.0</td>\n",
       "      <td>2019-05-30 22:17:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>746 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 20:47:09     21       3117.20         84.90        260.82   \n",
       "2018-11-01 21:03:09     21       3706.20         78.12        321.47   \n",
       "2018-11-01 21:13:09     24       4602.03         76.31        391.12   \n",
       "2018-11-02 21:34:11     30       4610.15         72.49        463.41   \n",
       "2018-11-03 14:20:13     21       3113.93         74.29        266.20   \n",
       "2018-11-03 20:16:13     21       2992.24         86.28        246.71   \n",
       "2018-11-03 22:01:13     22       3615.11        108.00        231.49   \n",
       "2018-11-03 22:42:13     28       4332.65         76.26        263.33   \n",
       "2018-11-05 15:49:17     24       3723.64         88.97        280.92   \n",
       "2018-11-05 19:33:17     21       2831.71         78.66        170.69   \n",
       "2018-11-06 20:49:20     21       3414.39         87.02        257.39   \n",
       "2018-11-08 15:56:23     21       3356.42         85.43        252.38   \n",
       "2018-11-08 20:50:23     23       3998.72         90.64        398.60   \n",
       "2018-11-08 20:51:23     21       3736.10         87.71        327.77   \n",
       "2018-11-08 20:59:23     21       3161.50         89.86        423.33   \n",
       "2018-11-09 20:49:25     21       3962.84        129.44        322.40   \n",
       "2018-11-09 21:41:25     21       3199.91         75.82        276.96   \n",
       "2018-11-09 22:09:25     22       3582.53        108.02        246.32   \n",
       "2018-11-10 20:07:26     22       3362.64         80.28        225.21   \n",
       "2018-11-10 21:17:26     21       3407.67        100.55        263.82   \n",
       "2018-11-10 21:48:26     21       3274.11         84.12        354.66   \n",
       "2018-11-10 22:03:26     21       3525.31        119.81        283.33   \n",
       "2018-11-11 17:02:28     21       3123.46         68.51        359.94   \n",
       "2018-11-11 20:45:28     21       3515.21         85.81        297.33   \n",
       "2018-11-11 20:48:28     21       3006.97         83.48        353.50   \n",
       "2018-11-11 22:17:28     23       3709.56         92.62        314.90   \n",
       "2018-11-12 16:28:30     22       3328.76         78.25        257.35   \n",
       "2018-11-12 21:01:30     21       3177.52         92.07        226.59   \n",
       "2018-11-12 21:06:30     21       3887.31        100.05        292.41   \n",
       "2018-11-13 15:51:32     23       3505.80         78.76        249.86   \n",
       "...                    ...           ...           ...           ...   \n",
       "2019-05-27 16:08:18     27      13177.00         80.89       2768.33   \n",
       "2019-05-27 18:29:18     23       5264.64         90.01        515.05   \n",
       "2019-05-27 19:28:18     21       4612.10         93.98        372.50   \n",
       "2019-05-27 19:49:18     28       5647.21         78.28        648.65   \n",
       "2019-05-27 20:03:18     21       5146.42         97.18       1250.87   \n",
       "2019-05-27 20:05:18     21       5242.64        113.51        507.65   \n",
       "2019-05-27 21:13:18     26       4656.33        102.24        300.69   \n",
       "2019-05-27 21:16:18     24       5160.23         95.19        538.70   \n",
       "2019-05-27 21:58:18     25       9587.37         97.71       1304.84   \n",
       "2019-05-27 22:01:18     21       5813.94        118.05       1130.25   \n",
       "2019-05-28 16:19:19     24       5168.07         94.52        869.76   \n",
       "2019-05-28 20:51:19     23       7090.56         89.50       1613.17   \n",
       "2019-05-28 20:52:19     23       5801.02         77.39        802.72   \n",
       "2019-05-28 22:53:19     22       4000.22         83.75        356.17   \n",
       "2019-05-29 16:02:20     23      10137.39         96.03       1245.05   \n",
       "2019-05-29 20:31:20     22       8799.29        105.93       2386.80   \n",
       "2019-05-29 21:12:20     21       4702.18         97.59        699.19   \n",
       "2019-05-29 21:34:20     24       5368.32         73.77        742.53   \n",
       "2019-05-29 22:46:20     21       6892.93        137.39       1309.64   \n",
       "2019-05-29 23:02:20     24       6331.52        103.16       1196.49   \n",
       "2019-05-30 20:02:21     24       5038.76         95.34        445.75   \n",
       "2019-05-30 20:16:21     26       6415.77         85.31        860.74   \n",
       "2019-05-30 21:17:21     23       4954.28         97.52        427.05   \n",
       "2019-05-30 21:24:21     21       3977.18         93.16        383.06   \n",
       "2019-05-30 21:28:21     25       8782.18         98.49       2549.79   \n",
       "2019-05-30 21:33:21     27       6456.64         99.65        978.91   \n",
       "2019-05-30 21:43:21     21       6371.84         65.98       1175.37   \n",
       "2019-05-30 21:47:21     21       3992.83         87.83        440.88   \n",
       "2019-05-30 21:53:21     24       8467.02        120.22       1511.17   \n",
       "2019-05-30 22:17:21     21       4926.35         85.01        826.90   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2018-11-01 20:47:09         148.0  2018-11-01 20:47:09  \n",
       "2018-11-01 21:03:09         176.0  2018-11-01 21:03:09  \n",
       "2018-11-01 21:13:09         191.0  2018-11-01 21:13:09  \n",
       "2018-11-02 21:34:11         153.0  2018-11-02 21:34:11  \n",
       "2018-11-03 14:20:13         148.0  2018-11-03 14:20:13  \n",
       "2018-11-03 20:16:13         142.0  2018-11-03 20:16:13  \n",
       "2018-11-03 22:01:13         164.0  2018-11-03 22:01:13  \n",
       "2018-11-03 22:42:13         154.0  2018-11-03 22:42:13  \n",
       "2018-11-05 15:49:17         155.0  2018-11-05 15:49:17  \n",
       "2018-11-05 19:33:17         134.0  2018-11-05 19:33:17  \n",
       "2018-11-06 20:49:20         162.0  2018-11-06 20:49:20  \n",
       "2018-11-08 15:56:23         159.0  2018-11-08 15:56:23  \n",
       "2018-11-08 20:50:23         173.0  2018-11-08 20:50:23  \n",
       "2018-11-08 20:51:23         177.0  2018-11-08 20:51:23  \n",
       "2018-11-08 20:59:23         150.0  2018-11-08 20:59:23  \n",
       "2018-11-09 20:49:25         188.0  2018-11-09 20:49:25  \n",
       "2018-11-09 21:41:25         152.0  2018-11-09 21:41:25  \n",
       "2018-11-09 22:09:25         162.0  2018-11-09 22:09:25  \n",
       "2018-11-10 20:07:26         152.0  2018-11-10 20:07:26  \n",
       "2018-11-10 21:17:26         162.0  2018-11-10 21:17:26  \n",
       "2018-11-10 21:48:26         155.0  2018-11-10 21:48:26  \n",
       "2018-11-10 22:03:26         167.0  2018-11-10 22:03:26  \n",
       "2018-11-11 17:02:28         148.0  2018-11-11 17:02:28  \n",
       "2018-11-11 20:45:28         167.0  2018-11-11 20:45:28  \n",
       "2018-11-11 20:48:28         143.0  2018-11-11 20:48:28  \n",
       "2018-11-11 22:17:28         161.0  2018-11-11 22:17:28  \n",
       "2018-11-12 16:28:30         151.0  2018-11-12 16:28:30  \n",
       "2018-11-12 21:01:30         151.0  2018-11-12 21:01:30  \n",
       "2018-11-12 21:06:30         185.0  2018-11-12 21:06:30  \n",
       "2018-11-13 15:51:32         152.0  2018-11-13 15:51:32  \n",
       "...                           ...                  ...  \n",
       "2019-05-27 16:08:18         488.0  2019-05-27 16:08:18  \n",
       "2019-05-27 18:29:18         228.0  2019-05-27 18:29:18  \n",
       "2019-05-27 19:28:18         219.0  2019-05-27 19:28:18  \n",
       "2019-05-27 19:49:18         201.0  2019-05-27 19:49:18  \n",
       "2019-05-27 20:03:18         245.0  2019-05-27 20:03:18  \n",
       "2019-05-27 20:05:18         249.0  2019-05-27 20:05:18  \n",
       "2019-05-27 21:13:18         179.0  2019-05-27 21:13:18  \n",
       "2019-05-27 21:16:18         215.0  2019-05-27 21:16:18  \n",
       "2019-05-27 21:58:18         383.0  2019-05-27 21:58:18  \n",
       "2019-05-27 22:01:18         276.0  2019-05-27 22:01:18  \n",
       "2019-05-28 16:19:19         215.0  2019-05-28 16:19:19  \n",
       "2019-05-28 20:51:19         308.0  2019-05-28 20:51:19  \n",
       "2019-05-28 20:52:19         252.0  2019-05-28 20:52:19  \n",
       "2019-05-28 22:53:19         181.0  2019-05-28 22:53:19  \n",
       "2019-05-29 16:02:20         440.0  2019-05-29 16:02:20  \n",
       "2019-05-29 20:31:20         399.0  2019-05-29 20:31:20  \n",
       "2019-05-29 21:12:20         223.0  2019-05-29 21:12:20  \n",
       "2019-05-29 21:34:20         223.0  2019-05-29 21:34:20  \n",
       "2019-05-29 22:46:20         328.0  2019-05-29 22:46:20  \n",
       "2019-05-29 23:02:20         263.0  2019-05-29 23:02:20  \n",
       "2019-05-30 20:02:21         209.0  2019-05-30 20:02:21  \n",
       "2019-05-30 20:16:21         246.0  2019-05-30 20:16:21  \n",
       "2019-05-30 21:17:21         215.0  2019-05-30 21:17:21  \n",
       "2019-05-30 21:24:21         189.0  2019-05-30 21:24:21  \n",
       "2019-05-30 21:28:21         351.0  2019-05-30 21:28:21  \n",
       "2019-05-30 21:33:21         239.0  2019-05-30 21:33:21  \n",
       "2019-05-30 21:43:21         303.0  2019-05-30 21:43:21  \n",
       "2019-05-30 21:47:21         190.0  2019-05-30 21:47:21  \n",
       "2019-05-30 21:53:21         352.0  2019-05-30 21:53:21  \n",
       "2019-05-30 22:17:21         234.0  2019-05-30 22:17:21  \n",
       "\n",
       "[746 rows x 6 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['count'] > 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f592460f28>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1']['res_time_avg'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f5924c6f28>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FrFq1iqVLlzJu3Lhqh2ZWEt8xbJZCb28vV1xxBcuXL+dzn/scy5cv54orrqC3t7faoZmVxEnALKXp06dzwQUXMGrUKC644AKmT59e7ZDMSuaOTLMUzjnnHO6++27uuusupk+fzubNm/nqV7/KOeecU+3QzEriJGCWwtixY3nvvfdYsmQJBw4c4LTTTmPMmDGMHTu22qGZlcTdQWYpbN++nfHjx9PY2IgkGhsbGT9+PNu3b692aGYlcRIwS2HMmDEsWbKErVu3sm7dOrZu3cqSJUsYM2ZMtUMzK4k3lTFLYdSoUUycOJHx48cfuVls7969vP322xw+fLja4ZkB3lTGbNA0NjZy4MABoLCEBMCBAwdobPRaiFabPDBsltLYsWNZvnz5kaWkr7322mqHZFYyJwGzFHbs2MHixYtZsGAB+/bto76+nhtvvJF777232qGZlcTdQWYpTJ48mVwux6RJkxg1ahSTJk0il8sxefLkaodmVhK3BMxSePfdd9m9ezf19fVEBO+99x67d+9m1Cj/PWW1yb9csxR27tzJhAkTOOOMMwA444wzmDBhAjt37qxyZGalcRIwS2nhwoWMGzcOSYwbN46FCxdWOySzkrk7yCylhx56iLvvvvvI2kF33HFHtUMyK5mTgFkKdXV1jB49+qi1g0477TQOHfL+SFab3B1klsLBgwc5cOAAEydOPHL38IEDBzh48GC1QzMriZOAWQr19fW0tLQwcWJhQ72JEyfS0tJCfX19lSMzK42TgFkK+/fvZ/Xq1Ud2Euvt7WX16tXs37+/ypGZlabsJCBpm6RfS3peUldSdo6kNZJeTp7PTsol6XuStkh6QdJHy72+2VBqbGxkz549bNu2jcOHD7Nt2zb27NnjtYOsZlWqJTAvImYVrVq3BFgbEdOAtcl7gAXAtOSxCFhaoeubDYldu3axf//+IzeHjRo1iv3797Nr164qR2ZWmsHqDroSuD95fT9wVVH5A1HwDHCWpEmDFINZxfV1A/UtG9337I3mrVZVIgkE8DNJGyQtSsoaIuINgOT5Q0l5I/B60bE9SZlZTSluCZjVskrcJ/DxiNgh6UPAGkkvnqSu+ik7blebJJksAmhoaCCfz1cgTLPKObYlAPh3ajWp7CQQETuS57ckPQZcDLwpaVJEvJF097yVVO8Bzis6fAqwo59zLgOWQWFnsblz55YbpllFnX766bz//vtHngH8O7VaVFZbVtI4SWf2vQYuAzYCK4Hrk2rXA48nr1cC1yWzhC4Bdvd1G5nVkr5/+PuezWpVuS2BBuCxZJu9OuAvIuKnkp4FHpbUCvwj8Pmk/ipgIbAFeBf44zKvb2ZmZSgrCUTEq8BF/ZS/DczvpzyAW8u5ppmZVY6nNpiZZZiTgJlZhjkJmJllmJOAmVmGOQmYmWWYk4CZWYY5CZiVwGsH2UjhPYbNgOSGxwHrb+2ggZ6jcLuM2fDgJGDGwP9hPtk/9P7H3WqR27JmKdx2222pys2GO7cEzFL4/ve/D8B9993Hvn37qK+v56abbjpSblZrNNybsM3NzdHV1VXtMMyOM3XJE2z79u9VOwyz40jaULTd70m5O8jMLMOcBMzMMsxJwMwsw5wEzMwyzEnAzCzDnATMzDLMScDMLMOcBMzMMqzkJCDpPEnrJXVL2iTpS0n5NyRtl/R88lhYdMydkrZIeknSZyrxBczMrHTlLBtxEPhKRDwn6Uxgg6Q1yWffjYg/La4saTpwDTADmAz8raSPRMShMmIwM7MylNwSiIg3IuK55PU7QDfQeJJDrgQejIh9EbEV2AJcXOr1zcysfBVZQE7SVOB3gP8HfBy4TdJ1QBeF1sIuCgnimaLDejhB0pC0CFgE0NDQQD6fr0SYliG3ru2l98DgX2fqkicG9fzjToMfzB83qNewbCs7CUgaDzwKfDki9khaCnwLiOT5O8CNQH8Lsfe7el1ELAOWQWEBublz55YbpmVM708Hf3G3fD7PYP82py55YtCvYdlW1uwgSadRSAA/joi/AoiINyPiUEQcBu7jX7p8eoDzig6fAuwo5/pmZlaecmYHCegAuiPiz4rKJxVVuxrYmLxeCVwjqV7S+cA04BelXt/MzMpXTnfQx4EvAL+W9HxS9t+AFkmzKHT1bAMWA0TEJkkPA5spzCy61TODzMyqq+QkEBGd9N/Pv+okx7QD7aVe08zMKst3DJuZZZj3GLYR6cymJfzb+5cM/oXuH9zTn9kE4C0sbfA4CdiI9E73t0fMFFGzweTuIDOzDHMSMDPLMHcH2Yg1JF0pPx3ca3zgjNMG9fxmTgI2Ig32eAAUksxQXMdsMLk7yMwsw5wEzMwyzEnAzCzDnATMzDLMScDMLMOcBMzMMsxJwMwsw5wEzMwyzDeLmQGFjfJKOO7/pD8mot+ttc2qwi0BMwr/MKd9rF+/vqTjzIYTJwEzswxzEjAzy7AhTwKSLpf0kqQtkoZg6yczMzuRIU0CkkYDPwAWANOBFknThzIGMzP7F0PdErgY2BIRr0bEfuBB4MohjsHMzBJDnQQagdeL3vckZWZmVgVDfZ9Af5Oxj5szJ2kRsAigoaGBfD4/yGGZpbd3717/Nq3mDXUS6AHOK3o/BdhxbKWIWAYsA2hubo65c+cOSXBmaeTzefzbtFqnobx5RVId8A/AfGA78CzwHyNi00mO+SfgtaGJ0CyVc4F/rnYQZv34cER8cCAVh7QlEBEHJd0GrAZGA8tPlgCSYwb0RcyGmqSuiGiudhxm5RjSloDZSOIkYCOB7xg2M8swJwGz0i2rdgBm5XJ3kJlZhrklYGaWYU4CZmYZ5iRglpB0VfGChpK+KenT1YzJbLB5TMBGBBX2h1REHC7jHCuAn0TEIxULzGyYc0vAapakqZK6Jd0DPAd8QdLTkp6T9JeSxif1vi1ps6QXJP3pCc71MeAK4G5Jz0v615JWSPqD5PNtkv5Xcv4uSR+VtFrSK5JuLjrPHZKeTa71P04R/19L2iBpU7JeFpJukXRXUZ0bJH0/ef3fJb0oaY2knKT/Wt5/QTMnAat9FwIPAJcCrcCnI+KjQBfwXySdA1wNzIiI3wb+Z38niYifAyuBOyJiVkS80k+11yPid4G/A1YAfwBcAnwTQNJlwDQKS6bPAmZL+uRJYr8xImYDzcAXJU0EHgF+v6jOHwEPSWoG/gPwO8nnvknNKmKoF5Azq7TXIuIZSZ+lsFHR3xd6hhgDPA3sAd4HfiTpCeAnZVxrZfL8a2B8RLwDvCPpfUlnAZclj18m9cZTSApPneB8X5R0dfL6PGBa8l1elXQJ8DKFJPf3wJeAxyPiPQBJf1PG9zA7wknAal1v8ixgTUS0HFtB0sUUFi28BrgN+FSJ19qXPB8uet33vi6J4X9HxL2nOpGkucCngd+NiHcl5YHTk48fAv4QeBF4LCIiGfMwqzh3B9lI8QzwcUkXAEgaK+kjybjAByJiFfBlCt00J/IOcGYZMawGbiwai2iU9KET1P0AsCtJAP+GQrdSn78CrgJaKCQEgE7gc5JOT87/e2XEaXaEWwI2IkTEP0m6AchJqk+K/4TCP+yPSzqdwl/q//kkp3kQuE/SFyn096eN4WeSmoCnkz/c9wL/CXirn+o/BW6W9ALwEoUk1neeXZI2A9Mj4hdJ2bOSVgK/orC0ehewO22MZsfyFFGzGiFpfETslTSWwjjDooh4rtpxWW1zS8CsdixLbmY7HbjfCcAqwS0ByxxJbcDnjyn+y4hoH4RrTQTW9vPR/Ih4u9LXM0vLScDMLMM8O8jMLMOcBMzMMsxJwMwsw5wEzMwyzEnAzCzD/j/7aARha5ZVCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1'][['res_time_avg']].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agui\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:34:48</th>\n",
       "      <td>1</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.0</td>\n",
       "      <td>2019-05-01 00:34:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:49</th>\n",
       "      <td>17</td>\n",
       "      <td>19770.18</td>\n",
       "      <td>207.54</td>\n",
       "      <td>2974.52</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>2019-05-01 14:00:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:36:49</th>\n",
       "      <td>8</td>\n",
       "      <td>8799.92</td>\n",
       "      <td>96.59</td>\n",
       "      <td>3233.26</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>2019-05-01 18:36:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:09:49</th>\n",
       "      <td>6</td>\n",
       "      <td>7399.94</td>\n",
       "      <td>307.39</td>\n",
       "      <td>3153.02</td>\n",
       "      <td>1233.0</td>\n",
       "      <td>2019-05-01 19:09:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:10:49</th>\n",
       "      <td>13</td>\n",
       "      <td>23595.60</td>\n",
       "      <td>206.20</td>\n",
       "      <td>4664.84</td>\n",
       "      <td>1815.0</td>\n",
       "      <td>2019-05-01 19:10:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:38:49</th>\n",
       "      <td>15</td>\n",
       "      <td>16169.25</td>\n",
       "      <td>142.47</td>\n",
       "      <td>3624.26</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>2019-05-01 20:38:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2019-05-01 00:34:48      1       1694.47       1694.47       1694.47   \n",
       "2019-05-01 14:00:49     17      19770.18        207.54       2974.52   \n",
       "2019-05-01 18:36:49      8       8799.92         96.59       3233.26   \n",
       "2019-05-01 19:09:49      6       7399.94        307.39       3153.02   \n",
       "2019-05-01 19:10:49     13      23595.60        206.20       4664.84   \n",
       "2019-05-01 20:38:49     15      16169.25        142.47       3624.26   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2019-05-01 00:34:48        1694.0  2019-05-01 00:34:48  \n",
       "2019-05-01 14:00:49        1162.0  2019-05-01 14:00:49  \n",
       "2019-05-01 18:36:49        1099.0  2019-05-01 18:36:49  \n",
       "2019-05-01 19:09:49        1233.0  2019-05-01 19:09:49  \n",
       "2019-05-01 19:10:49        1815.0  2019-05-01 19:10:49  \n",
       "2019-05-01 20:38:49        1077.0  2019-05-01 20:38:49  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df['2019-5-1']\n",
    "df2[df['res_time_avg']> 1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2019-05-01 00:34:48\t1\t1694.47\t1694.47\t1694.47\t1694.0\t2019-05-01 00:34:48 定义位异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1'][['res_time_sum','res_time_min','res_time_max','res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "date = df['2019-5-1'].resample('20T').mean()\n",
    "date[['res_time_sum','res_time_min','res_time_max','res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#业务高峰时段 下午2-5 晚上 7-8 响应时间是上升的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1':'2019-5-10']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#每天的情况都差不多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "            ...\n",
       "            2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
       "           dtype='int64', name='created_at', length=884)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2019-5-1'].index.weekday # 0代表星期1，1代表星期二"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  \n",
       "created_at                                                       \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekday'] = df.index.weekday\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  weekend  \n",
       "created_at                                                                \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3    False  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3    False  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07        3    False  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07        3    False  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07        3    False  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekend'] = df['weekday'].isin({5,6})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    7.016846\n",
       "True     7.574989\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('weekend')['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#周末调用平均次数多，7.57"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend  created_at\n",
       "False    0              3.239120\n",
       "         1              1.668388\n",
       "         2              1.162551\n",
       "         3              1.086705\n",
       "         4              1.155556\n",
       "         5              1.136364\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.000000\n",
       "         9              1.080000\n",
       "         10             1.239011\n",
       "         11             2.031690\n",
       "         12             4.195845\n",
       "         13             6.668042\n",
       "         14             8.260503\n",
       "         15             8.934448\n",
       "         16             8.466504\n",
       "         17             6.784996\n",
       "         18             6.717731\n",
       "         19             8.655913\n",
       "         20            10.536496\n",
       "         21            10.846906\n",
       "         22             9.034164\n",
       "         23             5.946834\n",
       "True     0              3.467782\n",
       "         1              1.741849\n",
       "         2              1.161826\n",
       "         3              1.050000\n",
       "         4              1.076923\n",
       "         5              1.333333\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.071429\n",
       "         9              1.144928\n",
       "         10             1.254111\n",
       "         11             1.992958\n",
       "         12             4.031889\n",
       "         13             6.905772\n",
       "         14             8.851321\n",
       "         15             9.858422\n",
       "         16             9.420550\n",
       "         17             7.334743\n",
       "         18             7.342150\n",
       "         19             9.270430\n",
       "         20            11.173609\n",
       "         21            11.695043\n",
       "         22            10.419916\n",
       "         23             7.025452\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#周末和非周末的时间对比\n",
    "df.groupby(['weekend',df.index.hour])['count'].mean().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>created_at</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekend</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>3.239120</td>\n",
       "      <td>1.668388</td>\n",
       "      <td>1.162551</td>\n",
       "      <td>1.086705</td>\n",
       "      <td>1.155556</td>\n",
       "      <td>1.136364</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.260503</td>\n",
       "      <td>8.934448</td>\n",
       "      <td>8.466504</td>\n",
       "      <td>6.784996</td>\n",
       "      <td>6.717731</td>\n",
       "      <td>8.655913</td>\n",
       "      <td>10.536496</td>\n",
       "      <td>10.846906</td>\n",
       "      <td>9.034164</td>\n",
       "      <td>5.946834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>3.467782</td>\n",
       "      <td>1.741849</td>\n",
       "      <td>1.161826</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>1.076923</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.071429</td>\n",
       "      <td>1.144928</td>\n",
       "      <td>...</td>\n",
       "      <td>8.851321</td>\n",
       "      <td>9.858422</td>\n",
       "      <td>9.420550</td>\n",
       "      <td>7.334743</td>\n",
       "      <td>7.342150</td>\n",
       "      <td>9.270430</td>\n",
       "      <td>11.173609</td>\n",
       "      <td>11.695043</td>\n",
       "      <td>10.419916</td>\n",
       "      <td>7.025452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "created_at        0         1         2         3         4         5    6   \\\n",
       "weekend                                                                       \n",
       "False       3.239120  1.668388  1.162551  1.086705  1.155556  1.136364  1.0   \n",
       "True        3.467782  1.741849  1.161826  1.050000  1.076923  1.333333  1.0   \n",
       "\n",
       "created_at   7         8         9   ...        14        15        16  \\\n",
       "weekend                              ...                                 \n",
       "False       1.0  1.000000  1.080000  ...  8.260503  8.934448  8.466504   \n",
       "True        1.0  1.071429  1.144928  ...  8.851321  9.858422  9.420550   \n",
       "\n",
       "created_at        17        18        19         20         21         22  \\\n",
       "weekend                                                                     \n",
       "False       6.784996  6.717731  8.655913  10.536496  10.846906   9.034164   \n",
       "True        7.334743  7.342150  9.270430  11.173609  11.695043  10.419916   \n",
       "\n",
       "created_at        23  \n",
       "weekend               \n",
       "False       5.946834  \n",
       "True        7.025452  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>weekend</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.239120</td>\n",
       "      <td>3.467782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.668388</td>\n",
       "      <td>1.741849</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.162551</td>\n",
       "      <td>1.161826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>1.086705</td>\n",
       "      <td>1.050000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1.076923</td>\n",
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       "      <td>1.136364</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.071429</td>\n",
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       "      <th>9</th>\n",
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       "      <td>1.144928</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
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       "      <td>1.254111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.031690</td>\n",
       "      <td>1.992958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.195845</td>\n",
       "      <td>4.031889</td>\n",
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       "      <th>13</th>\n",
       "      <td>6.668042</td>\n",
       "      <td>6.905772</td>\n",
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       "      <td>9.858422</td>\n",
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       "    <tr>\n",
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       "      <td>9.420550</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.784996</td>\n",
       "      <td>7.334743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.717731</td>\n",
       "      <td>7.342150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8.655913</td>\n",
       "      <td>9.270430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.536496</td>\n",
       "      <td>11.173609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.846906</td>\n",
       "      <td>11.695043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.034164</td>\n",
       "      <td>10.419916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.946834</td>\n",
       "      <td>7.025452</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekend         False      True \n",
       "created_at                      \n",
       "0            3.239120   3.467782\n",
       "1            1.668388   1.741849\n",
       "2            1.162551   1.161826\n",
       "3            1.086705   1.050000\n",
       "4            1.155556   1.076923\n",
       "5            1.136364   1.333333\n",
       "6            1.000000   1.000000\n",
       "7            1.000000   1.000000\n",
       "8            1.000000   1.071429\n",
       "9            1.080000   1.144928\n",
       "10           1.239011   1.254111\n",
       "11           2.031690   1.992958\n",
       "12           4.195845   4.031889\n",
       "13           6.668042   6.905772\n",
       "14           8.260503   8.851321\n",
       "15           8.934448   9.858422\n",
       "16           8.466504   9.420550\n",
       "17           6.784996   7.334743\n",
       "18           6.717731   7.342150\n",
       "19           8.655913   9.270430\n",
       "20          10.536496  11.173609\n",
       "21          10.846906  11.695043\n",
       "22           9.034164  10.419916\n",
       "23           5.946834   7.025452"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level = 0).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
